Songs Popularity Analysis Using Spotify Data: An exploratory study
Keywords:
Spotify, Music, Audio Features, Supervised Machine Learning, Unsupervised Machine LearningAbstract
This study presents an overview of analytical model for observing various factors which are impacting the songs popularity and predicting songs popularity using various machine learning algorithms. The data is collected using various methods. In most of the studies we found that researchers used Kaggle dataset and while others scrapped Spotify website to curate their own dataset.
We also found that maximum number of researchers predicted popularity of songs using same number of features of the songs i.e., Danceability, Tempo, Energy, Loudness, Speechiness, Acousticness, Instrumentalness, Liveness, Valence.
We also, observed that all the researchers used unsupervised and supervised machine learning algorithms to prognosticate songs popularity. In future, researchers can investigate the use of deep learning and other neural networks to observe the performance. We also recommend that choice of appropriate data features and loss functions can ensure optimized outcomes.
We also aim to analyse the preferences of the songs by users before and after covid pandemic.
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